Perivascular spaces (PVS), also known as the Virchow-Robin spaces, have been widely studied, which are defined as the pia-lined extensions of the subarachnoid space where subarachnoid CSF enters the brain. PVS surround penetrating arteries and continue along the outside of the penetrating arteries into white matter. Enlarged PVS are commonly observed in MR images in a number of neurological disorders. Normal PVS are typically not visible due to their small sizes, particularly in young adults. As a result, the physiological and pathophysiological significance of PVS remain elusive. Recently, several lines of evidence have suggested that PVS serve as part of the brain ?lymphatic? system through which interstitial solutes are cleared from the brain. Specifically, it has been demonstrated that arterial pulsation drives subarachnoid CSF flowing into the PVS and through which soluble proteins such as amyloid beta (A?) are cleared from the brain; dysfunction of PVS pathway thus may lead to enlarged PVS, an increased A? deposition, and subsequent neuronal dysfunction and loss, which clearly has profound implications in Alzheimer's diseases. While these recent studies have provided invaluable insights into the functions of PVS for cleaning interstitial solutes, invasive approaches such as two-photon microscopy or infusion of fluorescent and radio- labeled tracers were employed, which are not applicable to humans. Therefore, there are increasing needs of developing non-invasive approaches capable of revealing the PVS morphological (diameters, lengths and so on) and hemodynamic (velocity and arterial pulsation) features so as to allow direct assessments of the functional status of PVS. We have recently demonstrated that both the morphological and hemodynamic features of PVS in healthy young adults can be assessed using 7T. While our preliminary results demonstrate the feasibility of imaging PVS, in this application we propose to take steps further by developing imaging approaches capable of separately evaluating the morphological features of CSF and penetrating vessels (Aim 1). The ability of separately evaluating these two compartments in PVS will reveal if the CSF, penetrating vessels or both are altered in diseased populations. Furthermore, the flow velocity and arterial pulsatility of the penetrating arterioles in PVS will be measured in Aim 2 which could, potentially, enable the evaluation of how interstitial solutes are cleared from the brain. Finally, how these parameters are modified with aging will also be evaluated in Aims 1 and 2.
Perivascular spaces (PVS), also known as the Virchow-Robin spaces, have been widely studied. Enlarged PVS are commonly observed in MR images in a number of neurological disorders. Normal PVS are typically not visible due to their small sizes, particularly in young adults. As a result, the physiological and pathophysiological significance of PVS remain elusive. Recently, several lines of evidence have suggested that PVS serve as part of the brain ?lymphatic? system through which soluble proteins such as amyloid beta (A?) are cleared from the brain. Therefore, dysfunction of PVS pathway thus may lead to enlarged PVS, an increased A? deposition, and subsequent neuronal dysfunction and loss, which clearly has profound implications in Alzheimer's diseases. However, currently available approaches to discern PVS are invasive and thus are not applicable to humans. Therefore, there are increasing needs of developing non-invasive approaches capable of revealing the PVS morphological (diameters, lengths and so on) and hemodynamic (velocity and arterial pulsation) features so as to allow direct assessments of the functional status of PVS. To this end, this application proposes to develop non-invasive imaging approaches using 7T MRI to reveal morphological and hemodynamics features of PVS in healthy subjects between 18-55 years of age so as to provide insights into how PVS are altered with age.
| Zhang, Jun; Gao, Yaozong; Park, Sang Hyun et al. (2017) Structured Learning for 3-D Perivascular Space Segmentation Using Vascular Features. IEEE Trans Biomed Eng 64:2803-2812 |
